With the input of a \(32\times 32\)  feature map produced by the encoder, the decoder outputs a \(64\times 64\)  segmentation map. The decoder is comprised of two parts. The first part is a feature extraction block designed based on the Fast Super-Resolution Convolutional Neural Network (FSRCNN) [1]}, which includes five steps of feature extraction, shrinking, mapping, expanding and deconvolution. The feature extraction step utilizes 56 \(5\times 5\)  filters to first perform feature extraction on the input feature map. Then, the shrinking step adopts a smaller number of filters to reduce parameters and simplify the model, during which the feature dimension is reduced from 56 to 12. Next, the non-linear mapping part of 4 convolution layers of \(3\times 3\)  maps fuzzy features into clear features without changing feature map channels. Further, the expansion step adopts \(1\times 1\)  filters to extend the dimensions of the mapped high-resolution feature map and the number of channels back to 56, which is conducive to the reconstruction of a clearer segmentation map. Finally, the deconvolution step upsamples and aggregates the features using a set of deconvolution filters.
